Snowflake SnowPro Advanced Data Engineer DEA-C02 - SnowPro Advanced Data Engineer DEA-C02 Exam
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Total 65 questions
Question #11 (Topic: Exam A)
A Data Engineer needs to create a stage that will be used to ingest files from a Customer Relationship Management (CRM) system.
These are the requirements for the stage:
1. Use of an internal named stage with different folders that will be the repositories for the files
2. The files must be encrypted by the cloud service hosting the Snowflake account when they arrive on the stage
3. The files will be queried later using pre-signed URLs
4. Ability to query a metadata list of all the unstructured files on the stage
Which statement will meet these requirements?
These are the requirements for the stage:
1. Use of an internal named stage with different folders that will be the repositories for the files
2. The files must be encrypted by the cloud service hosting the Snowflake account when they arrive on the stage
3. The files will be queried later using pre-signed URLs
4. Ability to query a metadata list of all the unstructured files on the stage
Which statement will meet these requirements?
A. 
B. 
C. 
D. 
B.
C.
D.
Answer: A
Question #12 (Topic: Exam A)
Which command will load all data successfully into the table named finance_dept_location?
A. 
B. 
C. 
D. 
B.
C.
D.
Answer: A
Question #13 (Topic: Exam A)
A Data Engineer is designing a solution that will send report files generated and stored in a Snowflake stage to a customer, using email. The files can only be available for download for 60 minutes as new files will be generated and the old ones become obsolete.
What is the recommended way to meet these requirements?
What is the recommended way to meet these requirements?
A. Share data using a scoped URL and the SYSTEM$SEND_EMAIL function.
B. Share data using pre-signed URL and the SYSTEM$SEND_EMAIL function.
C. Share data using a stage file URL and the SYSTEM$SEND_EMAIL function.
D. Share data using a scoped URL and write a custom Python script to send the email.
Answer: B
Question #14 (Topic: Exam A)
A Data Engineer is working to optimize the performance of end-users' queries about automobile shipment data for a portal web application that generates dashboards for different clients.
An internal team queries the information using order_date as a WHERE condition; the ingestion and natural clustering are performed in this column. However, the application recovers data using the client_id, warehouse, product_id, and destination_city. The Engineer needs to create materialized views with different cluster keys, and must choose two columns combined with client_id to optimize the first cluster key.
Which statements will provide all the information needed to select the MOST effective combination of columns to cluster? (Choose two.)
An internal team queries the information using order_date as a WHERE condition; the ingestion and natural clustering are performed in this column. However, the application recovers data using the client_id, warehouse, product_id, and destination_city. The Engineer needs to create materialized views with different cluster keys, and must choose two columns combined with client_id to optimize the first cluster key.
Which statements will provide all the information needed to select the MOST effective combination of columns to cluster? (Choose two.)
A. SELECT SYSTEM$CLUSTERING_DEPTH('shipments');
B. SELECT SYSTEM$CLUSTERING_DEPTH('shipments', '(client_id, warehouse, product_id)');
C. SELECT SYSTEM$CLUSTERING_DEPTH('shipments', ' (client_id, destination_city, product_id)')
D. SELECT SYSTEM$CLUSTERING_DEPTH('shipments', '(warehouse, product_id, destination_city)')
E. SELECT SYSTEM$CLUSTERING_INFORMATION('shipments', 3);
Answer: BC
Question #15 (Topic: Exam A)
A user ran a one-time AI_SUMMARIZE_AGG query on a large log table using a Cortex Al model. The query consumed more than 1,000 credits, even though it ran on a size Small virtual warehouse.
A Data Engineer has been asked to implement a Snowflake-native, automated alert that triggers whenever a single Cortex AI SQL query consumes 300 credits, so that the query text and model choice can be reviewed.
How should these requirements be met?
A Data Engineer has been asked to implement a Snowflake-native, automated alert that triggers whenever a single Cortex AI SQL query consumes 300 credits, so that the query text and model choice can be reviewed.
How should these requirements be met?
A. Create a scheduled task that queries SNOWFLAKE.ACCOUNT_USAGE.CORTEX_AISQL_USAGE_HISTORY for recent rows where CREDITS_USED > 300. Join those rows to
SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY to capture the SQL text, and use a notification integration in the task body to send an alert with the query and model details. B. Attach a resource monitor with a 300-credit limit to the virtual warehouse used to run the Cortex AI SQL. Configure the monitor to send a notification when the limit is reached, and use the resource monitor alerts to identify any Cortex query that exceeds 300 credits. C. Create a scheduled task that checks SNOWFLAKE.ACCOUNT_USAGE.METERING_DAILY_HISTORY for SERVICE_TYPE = 'AI_SERVICES ' and TOTAL_CREDITS > 300 on the current date, and send a notification whenever the daily AI_SERVICES usage exceeds the threshold so the team can review the queries that ran that day. D. Set a low STATEMENT_TIMEOUT_IN_SECONDS value for sessions that use Cortex AI SQL, based on how long a typical query would take to reach 300 credits, and use the statement timeout threshold to prevent any single Cortex query from exceeding the 300-credit threshold.
SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY to capture the SQL text, and use a notification integration in the task body to send an alert with the query and model details. B. Attach a resource monitor with a 300-credit limit to the virtual warehouse used to run the Cortex AI SQL. Configure the monitor to send a notification when the limit is reached, and use the resource monitor alerts to identify any Cortex query that exceeds 300 credits. C. Create a scheduled task that checks SNOWFLAKE.ACCOUNT_USAGE.METERING_DAILY_HISTORY for SERVICE_TYPE = 'AI_SERVICES ' and TOTAL_CREDITS > 300 on the current date, and send a notification whenever the daily AI_SERVICES usage exceeds the threshold so the team can review the queries that ran that day. D. Set a low STATEMENT_TIMEOUT_IN_SECONDS value for sessions that use Cortex AI SQL, based on how long a typical query would take to reach 300 credits, and use the statement timeout threshold to prevent any single Cortex query from exceeding the 300-credit threshold.
Answer: A
